Useful R functions

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Sandbox vectors

Let’s define some vectors which can be used for demonstrations:

manyNumbers <- sample( 1:1000, 20 )
manyNumbers

 [1] 314 565 513 700 529 625  55 279 927 423  49 237 470  47 607 883 234 516 636 326

manyNumbersWithNA <- sample( c( NA, NA, NA, manyNumbers ) )
manyNumbersWithNA

 [1] 529 927 326 423  NA  55 607 565  49  47 237 234  NA 513 314  NA 883 279 636 700 516 625 470

duplicatedNumbers <- sample( 1:5, 10, replace = TRUE )
duplicatedNumbers

 [1] 3 2 1 3 4 2 3 3 4 5

letters

 [1] "a" "b" "c" "d" "e" "f" "g" "h" "i" "j" "k" "l" "m" "n" "o" "p" "q" "r" "s" "t" "u" "v" "w" "x" "y"
[26] "z"

LETTERS

 [1] "A" "B" "C" "D" "E" "F" "G" "H" "I" "J" "K" "L" "M" "N" "O" "P" "Q" "R" "S" "T" "U" "V" "W" "X" "Y"
[26] "Z"

mixedLetters <- c( sample( letters, 5 ), sample( LETTERS, 5 ) )
mixedLetters

 [1] "p" "c" "q" "e" "m" "W" "O" "E" "N" "B"

Are all/any elements TRUE

• Input: logical vector
• Output: single logical value
• Task: try, understand what happens when you use manyNumbersWithNA instead of manyNumbers.

all( manyNumbers <= 1000 )

[1] TRUE

all( manyNumbers <= 500 )

[1] FALSE

any( manyNumbers > 1000 )

[1] FALSE

any( manyNumbers > 500 )

[1] TRUE

all( !is.na( manyNumbers ) )

[1] TRUE

any( is.na( manyNumbers ) )

[1] FALSE

Which elements are TRUE

Input: logical vector Output: vector of numbers (positions)

which( manyNumbers > 900 )

[1] 9

which( manyNumbersWithNA > 900 )

[1] 2

which( is.na( manyNumbersWithNA ) )

[1]  5 13 16

Filtering vector elements

• Input: any vector and filtering condition
• Output: elements of the input vector
• Note: several ways to get the same effect

manyNumbers[ manyNumbers > 900 ] # indexing by logical vector

[1] 927

manyNumbers[ which( manyNumbers > 900 ) ] # indexing by positions

[1] 927

somePositions <- which( manyNumbers > 900 )
manyNumbers[ somePositions ]

[1] 927

Are some elements among other elements

• Input: two vectors
• Output: a logical vector corresponding to the first input vector

"A" %in% LETTERS

[1] TRUE

c( "X", "Y", "Z" ) %in% LETTERS

[1] TRUE TRUE TRUE

all( c( "X", "Y", "Z" ) %in% LETTERS )

[1] TRUE

all( mixedLetters %in% LETTERS )

[1] FALSE

any( mixedLetters %in% LETTERS )

[1] TRUE

mixedLetters[ mixedLetters %in% LETTERS ]

[1] "W" "O" "E" "N" "B"

mixedLetters[ !( mixedLetters %in% LETTERS ) ]

[1] "p" "c" "q" "e" "m"

manyNumbers %in% 300:600

 [1]  TRUE  TRUE  TRUE FALSE  TRUE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE
[18]  TRUE FALSE  TRUE

which( manyNumbers %in% 300:600 )

[1]  1  2  3  5 10 13 18 20

sum( manyNumbers %in% 300:600 )

[1] 8

Pick one of two (three) depending on condition

• Input: a logical vector and two vectors additional vectors (for TRUE, for FALSE)
• Output: elements of the additional vectors
• Note: it can take care of NAs

if_else( manyNumbersWithNA >= 500, "large", "small" )

 [1] "large" "large" "small" "small" NA      "small" "large" "large" "small" "small" "small" "small"
[13] NA      "large" "small" NA      "large" "small" "large" "large" "large" "large" "small"

if_else( manyNumbersWithNA >= 500, "large", "small", "UNKNOWN" )

 [1] "large"   "large"   "small"   "small"   "UNKNOWN" "small"   "large"   "large"   "small"   "small"  
[11] "small"   "small"   "UNKNOWN" "large"   "small"   "UNKNOWN" "large"   "small"   "large"   "large"  
[21] "large"   "large"   "small"  

# here integer 0L is needed instead of real 0.0 
# manyNumbersWithNA contains integer numbers and the method complains
if_else( manyNumbersWithNA >= 500, manyNumbersWithNA, 0L ) 

 [1] 529 927   0   0  NA   0 607 565   0   0   0   0  NA 513   0  NA 883   0 636 700 516 625   0

Duplicates and unique elements

• Input: a vector

unique( duplicatedNumbers )

[1] 3 2 1 4 5

unique( c( NA, duplicatedNumbers, NA ) )

[1] NA  3  2  1  4  5

duplicated( duplicatedNumbers )

 [1] FALSE FALSE FALSE  TRUE FALSE  TRUE  TRUE  TRUE  TRUE FALSE

Positions of max/min elements

which.max( manyNumbersWithNA )

[1] 2

manyNumbersWithNA[ which.max( manyNumbersWithNA ) ]

[1] 927

which.min( manyNumbersWithNA )

[1] 10

manyNumbersWithNA[ which.min( manyNumbersWithNA ) ]

[1] 47

range( manyNumbersWithNA, na.rm = TRUE )

[1]  47 927

Sorting/ordering of vectors

manyNumbersWithNA

 [1] 529 927 326 423  NA  55 607 565  49  47 237 234  NA 513 314  NA 883 279 636 700 516 625 470

sort( manyNumbersWithNA )

 [1]  47  49  55 234 237 279 314 326 423 470 513 516 529 565 607 625 636 700 883 927

sort( manyNumbersWithNA, na.last = TRUE )

 [1]  47  49  55 234 237 279 314 326 423 470 513 516 529 565 607 625 636 700 883 927  NA  NA  NA

sort( manyNumbersWithNA, na.last = TRUE, decreasing = TRUE )

 [1] 927 883 700 636 625 607 565 529 516 513 470 423 326 314 279 237 234  55  49  47  NA  NA  NA

manyNumbersWithNA[1:5]

[1] 529 927 326 423  NA

order( manyNumbersWithNA[1:5] )

[1] 3 4 1 2 5

rank( manyNumbersWithNA[1:5] )

[1] 3 4 1 2 5

sort( mixedLetters )

 [1] "B" "c" "e" "E" "m" "N" "O" "p" "q" "W"

Ranking of vectors

manyDuplicates <- sample( 10:15, 10, replace = TRUE )
rank( manyDuplicates )

 [1] 3.0 9.0 3.0 5.0 3.0 6.5 9.0 1.0 6.5 9.0

rank( manyDuplicates, ties.method = "min" )

 [1] 2 8 2 5 2 6 8 1 6 8

rank( manyDuplicates, ties.method = "random" )

 [1]  3  8  2  5  4  7  9  1  6 10

Rounding numbers

v <- c( -1, -0.5, 0, 0.5, 1, rnorm( 10 ) )
v

 [1] -1.0000000 -0.5000000  0.0000000  0.5000000  1.0000000 -0.4625066  1.0000197  1.1298453 -0.9166387
[10]  0.6870685  0.7110857  0.9595941 -1.8271069  0.5652537 -0.4595038

round( v, 0 )

 [1] -1  0  0  0  1  0  1  1 -1  1  1  1 -2  1  0

round( v, 1 )

 [1] -1.0 -0.5  0.0  0.5  1.0 -0.5  1.0  1.1 -0.9  0.7  0.7  1.0 -1.8  0.6 -0.5

round( v, 2 )

 [1] -1.00 -0.50  0.00  0.50  1.00 -0.46  1.00  1.13 -0.92  0.69  0.71  0.96 -1.83  0.57 -0.46

floor( v )

 [1] -1 -1  0  0  1 -1  1  1 -1  0  0  0 -2  0 -1

ceiling( v )

 [1] -1  0  0  1  1  0  2  2  0  1  1  1 -1  1  0

Naming vector elements

heights <- c( Amy = 166, Eve = 170, Bob = 177 )
heights

Amy Eve Bob 
166 170 177 

names( heights )

[1] "Amy" "Eve" "Bob"

names( heights ) <- c( "AMY", "EVE", "BOB" )
heights

AMY EVE BOB 
166 170 177 

heights[[ "EVE" ]]

[1] 170

Generating grids

expand_grid( x = c( 1:3, NA ), y = c( "a", "b" ) )

# A tibble: 8 × 2
      x y    
  <int> <chr>
1     1 a    
2     1 b    
3     2 a    
4     2 b    
5     3 a    
6     3 b    
7    NA a    
8    NA b    

Generating combinations

combn( c( "a", "b", "c", "d", "e" ), m = 2, simplify = TRUE )

     [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10]
[1,] "a"  "a"  "a"  "a"  "b"  "b"  "b"  "c"  "c"  "d"  
[2,] "b"  "c"  "d"  "e"  "c"  "d"  "e"  "d"  "e"  "e"  

combn( c( "a", "b", "c", "d", "e" ), m = 3, simplify = TRUE )

     [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10]
[1,] "a"  "a"  "a"  "a"  "a"  "a"  "b"  "b"  "b"  "c"  
[2,] "b"  "b"  "b"  "c"  "c"  "d"  "c"  "c"  "d"  "d"  
[3,] "c"  "d"  "e"  "d"  "e"  "e"  "d"  "e"  "e"  "e"  

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